Semantic matching between a source screen or source data and a target screen using semantic artificial intelligence

ABSTRACT

Semantic matching between a source screen or source data and a target screen using semantic artificial intelligence (AI) for robotic process automation (RPA) workflows is disclosed. The source data or source screen and the target screen are selected on a matching interface, semantic matching is performed between the source data/screen and the target screen using an artificial intelligence/machine learning (AI/ML) model, and matching graphical elements and unmatched graphical elements are highlighted, allowing the developer to see which graphical elements match and which do not. The matching interface may also provide a confidence score of the individual matches, provide an overall mapping score, and allow the developer to hide/unhide the matched/unmatched graphical elements. Activities of an RPA workflow may be automatically created based on the semantic mapping that can be executed to perform the automation.

FIELD

The present invention generally relates to semantic matching, and morespecifically, to semantic matching between a source screen or sourcedata and a target screen using semantic artificial intelligence (AI) forrobotic process automation (RPA) workflows.

BACKGROUND

Currently, developers need to manually create robotic process automation(RPA) workflows in an RPA designer application using activities. Whilecreating the RPA workflow, the developer needs to indicate the targetgraphical element on the screen, which causes the RPA designerapplication to automatically generate a selector corresponding to thetarget element with a set of anchors. Although activity recommendationand suggestion functionality currently exists in UiPath Studio™, forexample, fully automated RPA workflow creation is not supported.Indicating all of the target graphical elements manually while creatingthe RPA workflow is time consuming. Accordingly, an improved approach tocreating RPA workflows may be beneficial.

SUMMARY

Certain embodiments of the present invention may provide solutions tothe problems and needs in the art that have not yet been fullyidentified, appreciated, or solved by current RPA workflow generationtechnologies. For example, some embodiments of the present inventionpertain to semantic matching between a source screen or source data anda target screen using semantic AI for RPA workflows.

In an embodiment, a non-transitory computer-readable medium stores acomputer program. The computer program is configured to cause at leastone processor to receive a selection of a source screen or source dataand receive a selection of a target screen. The computer program is alsoconfigured to cause the at least one processor to call one or more AI/MLmodels that have been trained to perform semantic matching betweenlabels in the source screen and labels in the target screen, betweendata elements in the source data and the labels in the target screen, orboth. The computer program is further configured to cause the at leastone processor to receive indications of graphical elements associatedwith semantically matched labels in the target screen and respectiveconfidence scores from the one or more AI/ML models and display thegraphical elements associated with the semantically matched labels onthe target screen in a matching interface.

In another embodiment, a computer-implemented method for performingsemantic AI for RPA includes calling, by an RPA designer application,one or more AI/ML models that have been trained to perform semanticmatching between labels in a source screen and labels in a targetscreen, between data elements in the source data and the labels in thetarget screen, or both. The computer-implemented method also includesreceiving, by the RPA designer application, indications of graphicalelements associated with semantically matched labels in the targetscreen and respective confidence scores from the one or more AI/MLmodels and displaying the graphical elements associated with thesemantically matched labels on the target screen in a matchinginterface, by the RPA designer application. The computer-implementedmethod further includes automatically generating one or more activitiesin an RPA workflow that copy data from fields of the source screen orthe data elements of the source data into fields of the target screenhaving labels that the one or more AI/ML models identified assemantically matching the labels from the source screen or the dataelements from the source data, by the RPA designer application.

In yet another embodiment, a computing system includes memory storingcomputer program instructions for performing semantic AI for RPA and atleast one processor configured to execute the computer programinstructions. The computer program instructions are configured to causethe at least one processor to receive indications of graphical elementsassociated with semantically matched labels in a target screen andrespective confidence scores from one or more AI/ML models that havebeen trained to perform semantic matching between labels in the sourcescreen and labels in the target screen, between data elements in thesource data and the labels in the target screen, or both. The computerprogram instructions are also configured to cause the at least oneprocessor to display the graphical elements associated with thesemantically matched labels on the target screen in a matchinginterface. The computer program instructions are further configured tocause the at least one processor to receive a correction to a graphicalelement in the target screen identified by the one or more AI/ML modelsas having an associated semantically matching label, receive anindication of a new element in the target screen that was notsemantically matched to a label in the source screen by the one or moreAI/ML models, or both. Additionally, the computer program instructionsare configured to cause the at least one processor to collectinformation pertaining to the corrected and/or newly labeled graphicalelement in the target screen and the associated label and directly orindirectly store the collected information for retraining of the one ormore AI/ML models.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of certain embodiments of the inventionwill be readily understood, a more particular description of theinvention briefly described above will be rendered by reference tospecific embodiments that are illustrated in the appended drawings.While it should be understood that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings, in which:

FIG. 1 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 2 is an architectural diagram illustrating a deployed RPA system,according to an embodiment of the present invention.

FIG. 3 is an architectural diagram illustrating the relationship betweena designer, activities, and drivers, according to an embodiment of thepresent invention.

FIG. 4 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 5 is an architectural diagram illustrating a computing systemconfigured to perform semantic matching between a source screen orsource data and a target screen using semantic AI for RPA workflows,according to an embodiment of the present invention.

FIG. 6 is architectural diagram illustrating a system configured totrain artificial intelligence/machine learning (AI/ML) models andperform semantic matching between a source screen or source data and atarget screen using semantic AI for RPA workflows, according to anembodiment of the present invention.

FIG. 7A illustrates an example of a neural network that has been trainedto recognize graphical elements in an image, according to an embodimentof the present invention.

FIG. 7B illustrates an example of a neuron, according to an embodimentof the present invention.

FIG. 8 is a flowchart illustrating a process for training AI/ML model(s)to perform semantic matching between a source screen or source data anda target screen using semantic AI for RPA workflows, according to anembodiment of the present invention.

FIGS. 9A-G illustrate a matching interface for an RPA designerapplication, according to an embodiment of the present invention.

FIG. 10 illustrates an RPA designer application with an automaticallygenerated RPA workflow, according to an embodiment of the presentinvention.

FIG. 11A-N illustrate screens of an example semantic copy and pasteinterface, according to an embodiment of the present invention.

FIG. 12 is an architectural diagram illustrating an architecture of theAI/ML models for performing semantic AI, according to an embodiment ofthe present invention.

FIG. 13 is a flowchart illustrating a process for performing semanticmatching between a source screen or source data and a target screenusing semantic AI for RPA workflows, according to an embodiment of thepresent invention.

FIG. 14 is a flowchart illustrating a process for performing semanticmatching between a source screen or source data and a target screenusing semantic AI for using an attended automation interface, accordingto an embodiment of the present invention.

Unless otherwise indicated, similar reference characters denotecorresponding features consistently throughout the attached drawings.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Some embodiments pertain to semantic matching between a source screen orsource data and a target screen using semantic AI for RPA workflows. Forinstance, in some embodiments, the source data or source screen and thetarget screen are selected on a matching interface, semantic matching isperformed between the source data/screen and the target screen using anartificial intelligence/machine learning (AI/ML) model, and matchinggraphical elements and unmatched graphical elements are highlightedusing different colors (e.g., green and red, respectively), allowing thedeveloper to see which graphical elements match and which do not. Thematching interface also has additional features in some embodiments,such as providing a confidence score of the individual matches,providing an overall mapping score, and allowing the developer tohide/unhide the matched/unmatched graphical elements. Further, one ormore RPA workflow activities are automatically created in someembodiments based on the semantic mapping that can be executed toperform the semantic AI functionality as part of an automation executedby an RPA robot.

In some embodiments, a list of data fields may be obtained, such as froman Excel® spreadsheet, a relational database, a flat file source, etc.The semantic matching AI/ML model may iterate over the entries in thedata and fill them into the target screen. The semantic matching AI/MLmodel may be trained to do this regardless of the type of the datasource. Due to the semantic matching functionality of this AI/ML model,a 1-to-1 matching may not be required. For instance, a natural languagemodel may seek to match identical or similar names/phrases in a targetscreen to those in the source data (or start with the source data andlook for similar names/phrases in the target screen). In certainembodiments, an extensive set of training data is used to make thesemantic matching AI/ML model more accurate since there may be manysimilar words or phrases for certain terms and there may also be manydifferent subsets depending on context. In some embodiments, context mayalso be used. For instance, the semantic matching AI/ML model may learnthat a given target pertains to banking details vs. an invoice, vs. apurchase order vs. contact information, etc.

The semantic matching AI/ML model of some embodiments may be deployed toassist RPA developers during design time. However, in some embodiments,the semantic matching AI/ML model may be used at runtime to provide morerobust functionality and self-healing. This may be employed if the UIdescriptor fails to identify the target graphical element at runtimerather than generally in some embodiments since UI descriptors tend tobe considerably faster than semantic matching AI/ML models. As such, UIdescriptors should be employed first for the same or similar targetscreens.

For instance, if a given target element cannot be identified by a givenuser interface (UI) descriptor at runtime, such as if a UI changes dueto a new version of a target application, the semantic matching AI/MLmodel may be used to attempt to identify the target graphical element.This information may then be added as a synonym for the word or phraseof interest, and the UI descriptor for that graphical element may beupdated such that the UI descriptor will work going forward. If the userinterface changes yet again and the changed graphical element and/oranchor(s) are similar enough, the RPA robot may be able to identify thetarget graphical element in the new version of the application. See U.S.patent application Publication Ser. No. 16/922,289, for example.

A UI descriptor is a set of instructions for finding a UI element. UIdescriptors in some embodiments are an encapsulated data/struct formatthat includes UI element selector(s), anchor selector(s), computervision (CV) descriptor(s), unified target descriptor(s), a screen imagecapture (context), an element image capture, other metadata (e.g., theapplication and application version), a combination thereof, etc. Theencapsulated data/struct format may be extensible with future updates tothe platform and is not limited to the above definition. Any suitable UIdescriptor for identifying a UI element on a screen may be used withoutdeviating from the scope of the invention.

In some embodiments, what the semantic matching AI/ML model is detectingmay be combined with unified target descriptors for runtime detection.For such embodiments, in addition to words and phrases for the sourceand target, once mappings are confirmed, unified target information maybe collected. At runtime, the unified target descriptor may be triedfirst, and if not successful, the semantic matching AI/ML model may beused.

Unified target descriptors tend to be more stable and accurate thanAI/ML models. A unified target descriptor chains together multiple typesof UI descriptors. Unified target information includes UI descriptorinformation that facilitates identification of graphical elements forthe UI descriptor(s) that are employed.

A unified target descriptor may function like a finite state machine(FSM), where in a first context, a first UI descriptor mechanism isapplied, in a second context, a second UI descriptor is applied, etc. Inother words, the UI descriptors may work with a unified target thatencompasses some or all UI detection mechanisms through which imagedetection and definition are performed in some embodiments. The unifiedtarget may merge multiple techniques of identifying and automating UIelements into a single cohesive approach. The unified target mayprioritize certain UI descriptor types in some embodiments, such asprioritizing selector-based and driver-based UI detection mechanisms andusing CV as a fallback to find a target UI element if the first twomechanisms are not successful.

In some embodiments, a natural language processing (NLP) AI/ML model maybe used in addition to or in lieu of a semantic matching AI/ML model. Incertain embodiments, these AI/ML models may be used together. Forinstance, of one of the models meets or exceeds a certain threshold, ifthe average of both of the models meets or exceeds a threshold, etc., amatch may be proposed to the user.

In some embodiments, feedback loop functionality is provided. Forinstance, if a user adds a match or corrects a match proposed by thesemantic matching AI/ML model, information pertaining to this match maybe saved. This information may include, but is not limited to, ascreenshot of the target application, the label in the targetapplication and the corresponding label in the source screen or sourcedata, the label of the incorrect match, etc. The context may also becaptured, such as that the correction occurred for a webpage, SAP®, etc.This data may be used in conjunction with other labeled data collectedin this manner to retrain the semantic matching AI/ML model.

FIG. 1 is an architectural diagram illustrating an RPA system 100,according to an embodiment of the present invention. RPA system 100includes a designer 110 that allows a developer to design and implementworkflows. Designer 110 may provide a solution for applicationintegration, as well as automating third-party applications,administrative Information Technology (IT) tasks, and business ITprocesses. Designer 110 may facilitate development of an automationproject, which is a graphical representation of a business process.Simply put, designer 110 facilitates the development and deployment ofworkflows and robots.

The automation project enables automation of rule-based processes bygiving the developer control of the execution order and the relationshipbetween a custom set of steps developed in a workflow, defined herein as“activities.” One commercial example of an embodiment of designer 110 isUiPath Studio™. Each activity may include an action, such as clicking abutton, reading a file, writing to a log panel, etc. In someembodiments, workflows may be nested or embedded.

Some types of workflows may include, but are not limited to, sequences,flowcharts, Finite State Machines (FSMs), and/or global exceptionhandlers. Sequences may be particularly suitable for linear processes,enabling flow from one activity to another without cluttering aworkflow. Flowcharts may be particularly suitable to more complexbusiness logic, enabling integration of decisions and connection ofactivities in a more diverse manner through multiple branching logicoperators. FSMs may be particularly suitable for large workflows. FSMsmay use a finite number of states in their execution, which aretriggered by a condition (i.e., transition) or an activity. Globalexception handlers may be particularly suitable for determining workflowbehavior when encountering an execution error and for debuggingprocesses.

Once a workflow is developed in designer 110, execution of businessprocesses is orchestrated by conductor 120, which orchestrates one ormore robots 130 that execute the workflows developed in designer 110.One commercial example of an embodiment of conductor 120 is UiPathOrchestrator™. Conductor 120 facilitates management of the creation,monitoring, and deployment of resources in an environment. Conductor 120may act as an integration point with third-party solutions andapplications.

Conductor 120 may manage a fleet of robots 130, connecting and executingrobots 130 from a centralized point. Types of robots 130 that may bemanaged include, but are not limited to, attended robots 132, unattendedrobots 134, development robots (similar to unattended robots 134, butused for development and testing purposes), and nonproduction robots(similar to attended robots 132, but used for development and testingpurposes). Attended robots 132 are triggered by user events and operatealongside a human on the same computing system. Attended robots 132 maybe used with conductor 120 for a centralized process deployment andlogging medium. Attended robots 132 may help the human user accomplishvarious tasks, and may be triggered by user events. In some embodiments,processes cannot be started from conductor 120 on this type of robotand/or they cannot run under a locked screen. In certain embodiments,attended robots 132 can only be started from a robot tray or from acommand prompt. Attended robots 132 should run under human supervisionin some embodiments.

Unattended robots 134 run unattended in virtual environments and canautomate many processes. Unattended robots 134 may be responsible forremote execution, monitoring, scheduling, and providing support for workqueues. Debugging for all robot types may be run in designer 110 in someembodiments. Both attended and unattended robots may automate varioussystems and applications including, but not limited to, mainframes, webapplications, VMs, enterprise applications (e.g., those produced bySAP®, SalesForce®, Oracle®, etc.), and computing system applications(e.g., desktop and laptop applications, mobile device applications,wearable computer applications, etc.).

Conductor 120 may have various capabilities including, but not limitedto, provisioning, deployment, configuration, queueing, monitoring,logging, and/or providing interconnectivity. Provisioning may includecreating and maintenance of connections between robots 130 and conductor120 (e.g., a web application). Deployment may include assuring thecorrect delivery of package versions to assigned robots 130 forexecution. Configuration may include maintenance and delivery of robotenvironments and process configurations. Queueing may include providingmanagement of queues and queue items. Monitoring may include keepingtrack of robot identification data and maintaining user permissions.Logging may include storing and indexing logs to a database (e.g., anSQL database) and/or another storage mechanism (e.g., ElasticSearch®,which provides the ability to store and quickly query large datasets).Conductor 120 may provide interconnectivity by acting as the centralizedpoint of communication for third-party solutions and/or applications.

Robots 130 are execution agents that run workflows built in designer110. One commercial example of some embodiments of robot(s) 130 isUiPath Robots™. In some embodiments, robots 130 install the MicrosoftWindows® Service Control Manager (SCM)—managed service by default. As aresult, such robots 130 can open interactive Windows® sessions under thelocal system account, and have the rights of a Windows® service.

In some embodiments, robots 130 can be installed in a user mode. Forsuch robots 130, this means they have the same rights as the user underwhich a given robot 130 has been installed. This feature may also beavailable for High Density (HD) robots, which ensure full utilization ofeach machine at its maximum potential. In some embodiments, any type ofrobot 130 may be configured in an HD environment.

Robots 130 in some embodiments are split into several components, eachbeing dedicated to a particular automation task. The robot components insome embodiments include, but are not limited to, SCM-managed robotservices, user mode robot services, executors, agents, and command line.SCM-managed robot services manage and monitor Windows® sessions and actas a proxy between conductor 120 and the execution hosts (i.e., thecomputing systems on which robots 130 are executed). These services aretrusted with and manage the credentials for robots 130. A consoleapplication is launched by the SCM under the local system.

User mode robot services in some embodiments manage and monitor Windows®sessions and act as a proxy between conductor 120 and the executionhosts. User mode robot services may be trusted with and manage thecredentials for robots 130. A Windows® application may automatically belaunched if the SCM-managed robot service is not installed.

Executors may run given jobs under a Windows® session (i.e., they mayexecute workflows. Executors may be aware of per-monitor dots per inch(DPI) settings. Agents may be Windows® Presentation Foundation (WPF)applications that display the available jobs in the system tray window.Agents may be a client of the service. Agents may request to start orstop jobs and change settings. The command line is a client of theservice. The command line is a console application that can request tostart jobs and waits for their output.

Having components of robots 130 split as explained above helpsdevelopers, support users, and computing systems more easily run,identify, and track what each component is executing. Special behaviorsmay be configured per component this way, such as setting up differentfirewall rules for the executor and the service. The executor may alwaysbe aware of DPI settings per monitor in some embodiments. As a result,workflows may be executed at any DPI, regardless of the configuration ofthe computing system on which they were created. Projects from designer110 may also be independent of browser zoom level in some embodiments.For applications that are DPI-unaware or intentionally marked asunaware, DPI may be disabled in some embodiments.

FIG. 2 is an architectural diagram illustrating a deployed RPA system200, according to an embodiment of the present invention. In someembodiments, RPA system 200 may be, or may be a part of, RPA system 100of FIG. 1 . It should be noted that the client side, the server side, orboth, may include any desired number of computing systems withoutdeviating from the scope of the invention. On the client side, a robotapplication 210 includes executors 212, an agent 214, and a designer216. However, in some embodiments, designer 216 may not be running oncomputing system 210. Executors 212 are running processes. Severalbusiness projects may run simultaneously, as shown in FIG. 2 . Agent 214(e.g., a Windows® service) is the single point of contact for allexecutors 212 in this embodiment. All messages in this embodiment arelogged into conductor 230, which processes them further via databaseserver 240, indexer server 250, or both. As discussed above with respectto FIG. 1 , executors 212 may be robot components.

In some embodiments, a robot represents an association between a machinename and a username. The robot may manage multiple executors at the sametime. On computing systems that support multiple interactive sessionsrunning simultaneously (e.g., Windows® Server 2012), multiple robots maybe running at the same time, each in a separate Windows® session using aunique username. This is referred to as HD robots above.

Agent 214 is also responsible for sending the status of the robot (e.g.,periodically sending a “heartbeat” message indicating that the robot isstill functioning) and downloading the required version of the packageto be executed. The communication between agent 214 and conductor 230 isalways initiated by agent 214 in some embodiments. In the notificationscenario, agent 214 may open a Web Socket channel that is later used byconductor 230 to send commands to the robot (e.g., start, stop, etc.).

On the server side, a presentation layer (web application 232, Open DataProtocol (OData) Representative State Transfer (REST) ApplicationProgramming Interface (API) endpoints 234, and notification andmonitoring 236), a service layer (API implementation/business logic238), and a persistence layer (database server 240 and indexer server250) are included. Conductor 230 includes web application 232, ODataREST API endpoints 234, notification and monitoring 236, and APIimplementation/business logic 238. In some embodiments, most actionsthat a user performs in the interface of conductor 230 (e.g., viabrowser 220) are performed by calling various APIs. Such actions mayinclude, but are not limited to, starting jobs on robots,adding/removing data in queues, scheduling jobs to run unattended, etc.without deviating from the scope of the invention. Web application 232is the visual layer of the server platform. In this embodiment, webapplication 232 uses Hypertext Markup Language (HTML) and JavaScript(JS). However, any desired markup languages, script languages, or anyother formats may be used without deviating from the scope of theinvention. The user interacts with web pages from web application 232via browser 220 in this embodiment in order to perform various actionsto control conductor 230. For instance, the user may create robotgroups, assign packages to the robots, analyze logs per robot and/or perprocess, start and stop robots, etc.

In addition to web application 232, conductor 230 also includes servicelayer that exposes OData REST API endpoints 234. However, otherendpoints may be included without deviating from the scope of theinvention. The REST API is consumed by both web application 232 andagent 214. Agent 214 is the supervisor of one or more robots on theclient computer in this embodiment.

The REST API in this embodiment covers configuration, logging,monitoring, and queueing functionality. The configuration endpoints maybe used to define and configure application users, permissions, robots,assets, releases, and environments in some embodiments. Logging RESTendpoints may be used to log different information, such as errors,explicit messages sent by the robots, and other environment-specificinformation, for instance. Deployment REST endpoints may be used by therobots to query the package version that should be executed if the startjob command is used in conductor 230. Queueing REST endpoints may beresponsible for queues and queue item management, such as adding data toa queue, obtaining a transaction from the queue, setting the status of atransaction, etc.

Monitoring REST endpoints may monitor web application 232 and agent 214.Notification and monitoring API 236 may be REST endpoints that are usedfor registering agent 214, delivering configuration settings to agent214, and for sending/receiving notifications from the server and agent214. Notification and monitoring API 236 may also use WebSocketcommunication in some embodiments.

The persistence layer includes a pair of servers in thisembodiment—database server 240 (e.g., a SQL server) and indexer server250. Database server 240 in this embodiment stores the configurations ofthe robots, robot groups, associated processes, users, roles, schedules,etc. This information is managed through web application 232 in someembodiments. Database server 240 may manages queues and queue items. Insome embodiments, database server 240 may store messages logged by therobots (in addition to or in lieu of indexer server 250).

Indexer server 250, which is optional in some embodiments, stores andindexes the information logged by the robots. In certain embodiments,indexer server 250 may be disabled through configuration settings. Insome embodiments, indexer server 250 uses ElasticSearch®, which is anopen source project full-text search engine. Messages logged by robots(e.g., using activities like log message or write line) may be sentthrough the logging REST endpoint(s) to indexer server 250, where theyare indexed for future utilization.

FIG. 3 is an architectural diagram illustrating the relationship 300between a designer 310, activities 320, 330, drivers 340, and AI/MLmodels 350, according to an embodiment of the present invention. Per theabove, a developer uses designer 310 to develop workflows that areexecuted by robots. Workflows may include user-defined activities 320and UI automation activities 330. User-defined activities 320 and/or UIautomation activities 330 may call one or more AI/ML models 350 in someembodiments, which may be located locally to the computing system onwhich the robot is operating and/or remotely thereto. Some embodimentsare able to identify non-textual visual components in an image, which iscalled computer vision (CV) herein. Some CV activities pertaining tosuch components may include, but are not limited to, click, type, gettext, hover, element exists, refresh scope, highlight, etc. Click insome embodiments identifies an element using CV, optical characterrecognition (OCR), fuzzy text matching, and multi-anchor, for example,and clicks it. Type may identify an element using the above and types inthe element. Get text may identify the location of specific text andscan it using OCR. Hover may identify an element and hover over it.Element exists may check whether an element exists on the screen usingthe techniques described above. In some embodiments, there may behundreds or even thousands of activities that can be implemented indesigner 310. However, any number and/or type of activities may beavailable without deviating from the scope of the invention.

UI automation activities 330 are a subset of special, lower levelactivities that are written in lower level code (e.g., CV activities)and facilitate interactions with the screen. UI automation activities330 facilitate these interactions via drivers 340 and/or AI/ML models350 that allow the robot to interact with the desired software. Forinstance, drivers 340 may include OS drivers 342, browser drivers 344,VM drivers 346, enterprise application drivers 348, etc. One or more ofAI/ML models 350 may be used by UI automation activities 330 in order todetermine perform interactions with the computing system. In someembodiments, AI/ML models 350 may augment drivers 340 or replace themcompletely. Indeed, in certain embodiments, drivers 340 are notincluded.

Drivers 340 may interact with the OS at a low level looking for hooks,monitoring for keys, etc. They may facilitate integration with Chrome®,IE®, Citrix®, SAP®, etc. For instance, the “click” activity performs thesame role in these different applications via drivers 340.

FIG. 4 is an architectural diagram illustrating an RPA system 400,according to an embodiment of the present invention. In someembodiments, RPA system 400 may be or include RPA systems 100 and/or 200of FIGS. 1 and/or 2 . RPA system 400 includes multiple client computingsystems 410 running robots. Computing systems 410 are able tocommunicate with a conductor computing system 420 via a web applicationrunning thereon. Conductor computing system 420, in turn, is able tocommunicate with a database server 430 and an optional indexer server440.

With respect to FIGS. 1 and 3 , it should be noted that while a webapplication is used in these embodiments, any suitable client/serversoftware may be used without deviating from the scope of the invention.For instance, the conductor may run a server-side application thatcommunicates with non-web-based client software applications on theclient computing systems.

FIG. 5 is an architectural diagram illustrating a computing system 500configured to perform semantic matching between a source screen/sourcedata and a target screen using semantic AI for RPA workflows, accordingto an embodiment of the present invention. In some embodiments,computing system 500 may be one or more of the computing systemsdepicted and/or described herein. Computing system 500 includes a bus505 or other communication mechanism for communicating information, andprocessor(s) 510 coupled to bus 505 for processing information.Processor(s) 510 may be any type of general or specific purposeprocessor, including a Central Processing Unit (CPU), an ApplicationSpecific Integrated Circuit (ASIC), a Field Programmable Gate Array(FPGA), a Graphics Processing Unit (GPU), multiple instances thereof,and/or any combination thereof. Processor(s) 510 may also have multipleprocessing cores, and at least some of the cores may be configured toperform specific functions. Multi-parallel processing may be used insome embodiments. In certain embodiments, at least one of processor(s)510 may be a neuromorphic circuit that includes processing elements thatmimic biological neurons. In some embodiments, neuromorphic circuits maynot require the typical components of a Von Neumann computingarchitecture.

Computing system 500 further includes a memory 515 for storinginformation and instructions to be executed by processor(s) 510. Memory515 can be comprised of any combination of Random Access Memory (RAM),Read Only Memory (ROM), flash memory, cache, static storage such as amagnetic or optical disk, or any other types of non-transitorycomputer-readable media or combinations thereof. Non-transitorycomputer-readable media may be any available media that can be accessedby processor(s) 510 and may include volatile media, non-volatile media,or both. The media may also be removable, non-removable, or both.

Additionally, computing system 500 includes a communication device 520,such as a transceiver, to provide access to a communications network viaa wireless and/or wired connection. In some embodiments, communicationdevice 520 may be configured to use Frequency Division Multiple Access(FDMA), Single Carrier FDMA (SC-FDMA), Time Division Multiple Access(TDMA), Code Division Multiple Access (CDMA), Orthogonal FrequencyDivision Multiplexing (OFDM), Orthogonal Frequency Division MultipleAccess (OFDMA), Global System for Mobile (GSM) communications, GeneralPacket Radio Service (GPRS), Universal Mobile Telecommunications System(UMTS), cdma2000, Wideband CDMA (W-CDMA), High-Speed Downlink PacketAccess (HSDPA), High-Speed Uplink Packet Access (HSUPA), High-SpeedPacket Access (HSPA), Long Term Evolution (LTE), LTE Advanced (LTE-A),802.11x, Wi-Fi, Zigbee, Ultra-WideBand (UWB), 802.16x, 802.15, HomeNode-B (HnB), Bluetooth, Radio Frequency Identification (RFID), InfraredData Association (IrDA), Near-Field Communications (NFC), fifthgeneration (5G), New Radio (NR), any combination thereof, and/or anyother currently existing or future-implemented communications standardand/or protocol without deviating from the scope of the invention. Insome embodiments, communication device 520 may include one or moreantennas that are singular, arrayed, phased, switched, beamforming,beamsteering, a combination thereof, and or any other antennaconfiguration without deviating from the scope of the invention.

Processor(s) 510 are further coupled via bus 505 to a display 525, suchas a plasma display, a Liquid Crystal Display (LCD), a Light EmittingDiode (LED) display, a Field Emission Display (FED), an Organic LightEmitting Diode (OLED) display, a flexible OLED display, a flexiblesubstrate display, a projection display, a 4K display, a high definitiondisplay, a Retina® display, an In-Plane Switching (IPS) display, or anyother suitable display for displaying information to a user. Display 525may be configured as a touch (haptic) display, a three dimensional (3D)touch display, a multi-input touch display, a multi-touch display, etc.using resistive, capacitive, surface-acoustic wave (SAW) capacitive,infrared, optical imaging, dispersive signal technology, acoustic pulserecognition, frustrated total internal reflection, etc. Any suitabledisplay device and haptic I/O may be used without deviating from thescope of the invention.

A keyboard 530 and a cursor control device 535, such as a computermouse, a touchpad, etc., are further coupled to bus 505 to enable a userto interface with computing system 500. However, in certain embodiments,a physical keyboard and mouse may not be present, and the user mayinteract with the device solely through display 525 and/or a touchpad(not shown). Any type and combination of input devices may be used as amatter of design choice. In certain embodiments, no physical inputdevice and/or display is present. For instance, the user may interactwith computing system 500 remotely via another computing system incommunication therewith, or computing system 500 may operateautonomously.

Memory 515 stores software modules that provide functionality whenexecuted by processor(s) 510. The modules include an operating system540 for computing system 500. The modules further include a semanticmatching module 545 that is configured to perform all or part of theprocesses described herein or derivatives thereof. Computing system 500may include one or more additional functional modules 550 that includeadditional functionality.

One skilled in the art will appreciate that a “system” could be embodiedas a server, an embedded computing system, a personal computer, aconsole, a personal digital assistant (PDA), a cell phone, a tabletcomputing device, a quantum computing system, or any other suitablecomputing device, or combination of devices without deviating from thescope of the invention. Presenting the above-described functions asbeing performed by a “system” is not intended to limit the scope of thepresent invention in any way, but is intended to provide one example ofthe many embodiments of the present invention. Indeed, methods, systems,and apparatuses disclosed herein may be implemented in localized anddistributed forms consistent with computing technology, including cloudcomputing systems. The computing system could be part of or otherwiseaccessible by a local area network (LAN), a mobile communicationsnetwork, a satellite communications network, the Internet, a public orprivate cloud, a hybrid cloud, a server farm, any combination thereof,etc. Any localized or distributed architecture may be used withoutdeviating from the scope of the invention.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, include one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may include disparate instructions stored in differentlocations that, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, RAM, tape, and/or any other suchnon-transitory computer-readable medium used to store data withoutdeviating from the scope of the invention.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

FIG. 6 is an architectural diagram illustrating a system 600 configuredto train AI/ML models and perform semantic matching between a sourcescreen/source data and a target screen using semantic AI for RPAworkflows, according to an embodiment of the present invention. System600 includes user computing systems, such as desktop computer 602,tablet 604, and smart phone 606. However, any desired computing systemmay be used without deviating from the scope of invention including, butnot limited to, smart watches, laptop computers, Internet-of-Things(IoT) devices, vehicle computing systems, etc.

Each computing system 602, 604, 606 has an RPA designer applicationinstalled thereon. RPA designer application 610 provides mappingfunctionality that allows the respective user to select source screensor source data and target screens. RPA designer application 610 is alsoconfigured to call AI/ML models 632 of a server 630 via a network 620(e.g., a local area network (LAN), a mobile communications network, asatellite communications network, the Internet, any combination thereof,etc.). Server 630 stores data in and retrieves data from a database 640.

AL/ML models 632 provide semantic AI functionality, CV, OCR, NLP, etc..For instance, one AI/ML model may provide CV functionality, another mayperform OCR, yet another may use this data to perform semantic matching,etc.

FIG. 7A illustrates an example of a neural network 700 that has beentrained to recognize graphical elements in an image, according to anembodiment of the present invention. Here, neural network 700 receivespixels of a screenshot image of a 1920×1080 screen as input for input“neurons” 1 to I of the input layer. In this case, I is 2,073,600, whichis the total number of pixels in the screenshot image.

Neural network 700 also includes a number of hidden layers. Both DLNNsand SLNNs usually have multiple layers, although SLNNs may only have oneor two layers in some cases, and normally fewer than DLNNs. Typically,the neural network architecture includes an input layer, multipleintermediate layers, and an output layer, as is the case in neuralnetwork 700.

A DLNN often has many layers (e.g., 10, 50, 200, etc.) and subsequentlayers typically reuse features from previous layers to compute morecomplex, general functions. A SLNN, on the other hand, tends to haveonly a few layers and train relatively quickly since expert features arecreated from raw data samples in advance. However, feature extraction islaborious. DLNNs, on the other hand, usually do not require expertfeatures, but tend to take longer to train and have more layers.

For both approaches, the layers are trained simultaneously on thetraining set, normally checking for overfitting on an isolatedcross-validation set. Both techniques can yield excellent results, andthere is considerable enthusiasm for both approaches. The optimal size,shape, and quantity of individual layers varies depending on the problemthat is addressed by the respective neural network.

Returning to FIG. 7A, pixels provided as the input layer are fed asinputs to the J neurons of hidden layer 1. While all pixels are fed toeach neuron in this example, various architectures are possible that maybe used individually or in combination including, but not limited to,feed forward networks, radial basis networks, deep feed forwardnetworks, deep convolutional inverse graphics networks, convolutionalneural networks, recurrent neural networks, artificial neural networks,long/short term memory networks, gated recurrent unit networks,generative adversarial networks, liquid state machines, auto encoders,variational auto encoders, denoising auto encoders, sparse autoencoders, extreme learning machines, echo state networks, Markov chains,Hopfield networks, Boltzmann machines, restricted Boltzmann machines,deep residual networks, Kohonen networks, deep belief networks, deepconvolutional networks, support vector machines, neural Turing machines,or any other suitable type or combination of neural networks withoutdeviating from the scope of the invention.

Hidden layer 2 receives inputs from hidden layer 1, hidden layer 3receives inputs from hidden layer 2, and so on for all hidden layersuntil the last hidden layer provides its outputs as inputs for theoutput layer. It should be noted that numbers of neurons I, J, K, and Lare not necessarily equal, and thus, any desired number of layers may beused for a given layer of neural network 700 without deviating from thescope of the invention. Indeed, in certain embodiments, the types ofneurons in a given layer may not all be the same.

Neural network 700 is trained to assign a confidence score to graphicalelements believed to have been found in the image. In order to reducematches with unacceptably low likelihoods, only those results with aconfidence score that meets or exceeds a confidence threshold may beprovided in some embodiments. For instance, if the confidence thresholdis 80%, outputs with confidence scores exceeding this amount may be usedand the rest may be ignored. In this case, the output layer indicatesthat two text fields, a text label, and a submit button were found.Neural network 700 may provide the locations, dimensions, images, and/orconfidence scores for these elements without deviating from the scope ofthe invention, which can be used subsequently by an RPA robot or anotherprocess that uses this output for a given purpose.

It should be noted that neural networks are probabilistic constructsthat typically have a confidence score. This may be a score learned bythe AI/ML model based on how often a similar input was correctlyidentified during training. For instance, text fields often have arectangular shape and a white background. The neural network may learnto identify graphical elements with these characteristics with a highconfidence. Some common types of confidence scores include a decimalnumber between 0 and 1 (which can be interpreted as a percentage ofconfidence), a number between negative co and positive co, or a set ofexpressions (e.g., “low,” “medium,” and “high”). Various post-processingcalibration techniques may also be employed in an attempt to obtain amore accurate confidence score, such as temperature scaling, batchnormalization, weight decay, negative log likelihood (NLL), etc.

“Neurons” in a neural network are mathematical functions that that aretypically based on the functioning of a biological neuron. Neuronsreceive weighted input and have a summation and an activation functionthat governs whether they pass output to the next layer. This activationfunction may be a nonlinear thresholded activity function where nothinghappens if the value is below a threshold, but then the functionlinearly responds above the threshold (i.e., a rectified linear unit(ReLU) nonlinearity). Summation functions and ReLU functions are used indeep learning since real neurons can have approximately similar activityfunctions. Via linear transforms, information can be subtracted, added,etc. In essence, neurons act as gating functions that pass output to thenext layer as governed by their underlying mathematical function. Insome embodiments, different functions may be used for at least someneurons.

An example of a neuron 710 is shown in FIG. 7B. Inputs x₁, x₂, . . . ,x_(n), from a preceding layer are assigned respective weights w₁, w₂, .. . , w_(n). Thus, the collective input from preceding neuron 1 is w₁x₁.These weighted inputs are used for the neuron's summation functionmodified by a bias, such as:

$\begin{matrix}{{\sum\limits_{i = 1}^{m}\left( {w_{i}x_{i}} \right)} + {bias}} & (1)\end{matrix}$

This summation is compared against an activation function ƒ(x) todetermine whether the neuron “fires”. For instance, ƒ(x) may be givenby:

$\begin{matrix}{{f(x)} = \left\{ \begin{matrix}{{{1{if}{\sum{wx}}} + {bias}} \geq 0} \\{{{0{if}{\sum{wx}}} + {bias}} < 0}\end{matrix} \right.} & (2)\end{matrix}$

The output γ of neuron 710 may thus be given by:

$\begin{matrix}{y = {{{f(x)}{\sum\limits_{i = 1}^{m}\left( {w_{i}x_{i}} \right)}} + {bias}}} & (3)\end{matrix}$

In this case, neuron 710 is a single-layer perceptron. However, anysuitable neuron type or combination of neuron types may be used withoutdeviating from the scope of the invention.

The goal, or “reward function” is often employed, such as for this casethe successful identification of graphical elements in the image. Areward function explores intermediate transitions and steps with bothshort term and long term rewards to guide the search of a state spaceand attempt to achieve a goal (e.g., successful identification ofgraphical elements, successful identification of a next sequence ofactivities for an RPA workflow, etc.).

During training, various labeled data (in this case, images) are fedthrough neural network 700. Successful identifications strengthenweights for inputs to neurons, whereas unsuccessful identificationsweaken them. A cost function, such as mean square error (MSE) orgradient descent may be used to punish predictions that are slightlywrong much less than predictions that are very wrong. If the performanceof the AI/ML model is not improving after a certain number of trainingiterations, a data scientist may modify the reward function, provideindications of where non-identified graphical elements are, providecorrections of misidentified graphical elements, etc.

Backpropagation is a technique for optimizing synaptic weights in afeedforward neural network. Backpropagation may be used to “pop thehood” on the hidden layers of the neural network to see how much of theloss every node is responsible for, and subsequently updating theweights in such a way that minimizes the loss by giving the nodes withhigher error rates lower weights, and vice versa. In other words,backpropagation allows data scientists to repeatedly adjust the weightsso as to minimize the difference between actual output and desiredoutput.

The backpropagation algorithm is mathematically founded in optimizationtheory. In supervised learning, training data with a known output ispassed through the neural network and error is computed with a costfunction from known target output, which gives the error forbackpropagation. Error is computed at the output, and this error istransformed into corrections for network weights that will minimize theerror.

In the case of supervised learning, an example of backpropagation isprovided below. A column vector input x is processed through a series ofN nonlinear activity functions ƒ_(i) between each layer i=1, . . . , Nof the network, with the output at a given layer first multiplied by asynaptic matrix W_(i), and with a bias vector b_(i) added. The networkoutput o, given by

o=ƒ _(N)(w _(N)ƒ_(N-1)(W _(N-1)ƒ_(N-2)( . . . ƒ₁(W ₁ x+b ₁) . . . )+b_(N-1))+b _(N))  (4)

In some embodiments, o is compared with a target output t, resulting inan error E=½∥o−t∥², which is desired to be minimized.

Optimization in the form of a gradient descent procedure may be used tominimize the error by modifying the synaptic weights W_(i) for eachlayer. The gradient descent procedure requires the computation of theoutput o given an input x corresponding to a known target output t, andproducing an error o−t. This global error is then propagated backwardsgiving local errors for weight updates with computations similar to, butnot exactly the same as, those used for forward propagation. Inparticular, the backpropagation step typically requires an activityfunction of the form p_(j)(n_(j))=ƒ′_(j) (n_(j)), where n_(j) is thenetwork activity at layer j (i.e., n_(j)=W_(j)o_(j-1)+b_(j)) whereo_(j)=ƒ_(j) (n_(j)) and the apostrophe ' denotes the derivative of theactivity function ƒ.

The weight updates may be computed via the formulae:

$\begin{matrix}{d_{j} = \left\{ \begin{matrix}{{\left( {o - t} \right) \circ {p_{j}\left( n_{j} \right)}},} & {j = N} \\{{W_{j + 1}^{T}{d_{j + 1} \circ {p_{j}\left( n_{j} \right)}}},} & {j < N}\end{matrix} \right.} & (5)\end{matrix}$ $\begin{matrix}{\frac{\partial E}{\partial W_{j + 1}} = {d_{j + 1}\left( o_{j} \right)}^{T}} & (6)\end{matrix}$ $\begin{matrix}{\frac{\partial E}{\partial b_{j + 1}} = d_{j + 1}} & (7)\end{matrix}$ $\begin{matrix}{W_{j}^{new} = {W_{j}^{old} - {\eta\frac{\partial E}{\partial W_{j}}}}} & (8)\end{matrix}$ $\begin{matrix}{b_{j}^{new} = {b_{j}^{old} - {\eta\frac{\partial E}{\partial b_{j}}}}} & (9)\end{matrix}$

where o denotes a Hadamard product (i.e., the element-wise product oftwo vectors), ^(T) denotes the matrix transpose, and o_(j) denotesƒ_(j)(W_(j)o_(j-1)+b_(j)), with o₀=x. Here, the learning rate η ischosen with respect to machine learning considerations. Below, η isrelated to the neural Hebbian learning mechanism used in the neuralimplementation. Note that the synapses W and b can be combined into onelarge synaptic matrix, where it is assumed that the input vector hasappended ones, and extra columns representing the b synapses aresubsumed to W.

The AI/ML model is trained over multiple epochs until it reaches a goodlevel of accuracy (e.g., 97% or better using an F2 or F4 threshold fordetection and approximately 2,000 epochs). This accuracy level may bedetermined in some embodiments using an F1 score, an F2 score, an F4score, or any other suitable technique without deviating from the scopeof the invention. Once trained on the training data, the AI/ML model istested on a set of evaluation data that the AI/ML model has notencountered before. This helps to ensure that the AI/ML model is not“over fit” such that it identifies graphical elements in the trainingdata well, but does not generalize well to other images.

In some embodiments, it may not be known what accuracy level may beachieved. Accordingly, if the accuracy of the AI/ML model is starting todrop when analyzing the evaluation data (i.e., the model is performingwell on the training data, but is starting to perform less well on theevaluation data), the AI/ML model may go through more epochs of trainingon the training data (and/or new training data). In some embodiments,the AI/ML model is only deployed if the accuracy reaches a certain levelor if the accuracy of the trained AI/ML model is superior to an existingdeployed AI/ML model.

In certain embodiments, a collection of trained AI/ML models may be usedto accomplish a task, such as employing an AI/ML model for each type ofgraphical element of interest, employing an AI/ML model to perform OCR,deploying yet another AI/ML model to recognize proximity relationshipsbetween graphical elements, employing still another AI/ML model togenerate an RPA workflow based on the outputs from the other AI/MLmodels, etc. This may collectively allow the AI/ML models to enablesemantic automation, for instance.

Some embodiments may use transformer networks such asSentenceTransformers™, which is a Python™ framework for state-of-the-artsentence, text, and image embeddings. Such transformer networks learnassociations of words and phrases that have both high scores and lowscores. This trains the AI/ML model to determine what is close to theinput and what is not, respectively. Rather than just using pairs ofwords/phrases, transformer networks may use the field length and fieldtype, as well.

FIG. 8 is a flowchart illustrating a process 800 for training AI/MLmodel(s) to perform semantic matching between a source screen or sourcedata and a target screen using semantic AI for RPA workflows, accordingto an embodiment of the present invention. The process begins withproviding labeled screens (e.g., with graphical elements and textidentified), words and phrases, a “thesaurus” of semantic associationsbetween words and phrases such that similar words and phrases for agiven word or phrase can be identified, etc. at 810. The AI/ML model isthen trained over multiple epochs at 820 and results are reviewed at830.

If the AI/ML model fails to meet a desired confidence threshold at 840,the training data is supplemented and/or the reward function is modifiedto help the AI/ML model achieve its objectives better at 850 and theprocess returns to step 820. If the AI/ML model meets the confidencethreshold at 840, the AI/ML model is tested on evaluation data at 860 toensure that the AI/ML model generalizes well and that the AI/ML model isnot over fit with respect to the training data. The evaluation data mayinclude source screens, source data, and target screens that the AI/ML,model has not processed before. If the confidence threshold is met at870 for the evaluation data, the AI/ML model is deployed at 880. If not,the process returns to step 850 and the AI/ML model is trained further.

Some embodiments bring semantic automation into automation platforms forcreating fully automated workflows with less or minimal interactioninput from the developer. Using semantic mapping, the UI fields from adata source/source screen are mapped to UI fields on the target screensemantically using one or more AI/ML models, and fully automatedworkflows can be created from this semantic mapping without interventionby the developer. In a current prototype, the mapping can be achievedfor up to 80% of the UI fields, and with developer assistance, theremaining ˜20% can be mapped. The AI/ML model(s) can be retrained tolearn to match the UI fields more accurately over time, with theexpectation that the mapping will approach 100% accuracy in the future.

In some embodiments, the RPA designer application includes a semanticmatching feature that allows RPA developers to perform a match betweentwo screens or between data (e.g., customer data) and a screen. This maybe implemented as a “semantic AI” button on a ribbon, dropdown menu, oranother suitable user interface element, for example. Upon selection ofsemantic AI functionality, the RPA designer application may display amatching interface, such as matching interface 900 of FIGS. 9A-G. Whilethis is a general example, many use cases exist for the semantic AIprovided by embodiments of the present invention, such as mapping aninvoice to SAP®, automatically inputting data from an excel spreadsheetinto a CRM application, mapping XAML from an RPA workflow to another RPAworkflow, etc. Also, while this example consists of text fields, othergraphical elements, such as buttons, text areas, etc. may be mappedwithout deviating from the scope of the invention.

Matching interface 900 includes a mapping options pane 910 and a mappingpane 920. When the developer selects map screens option 912, a selectsource button 922 and a select target button 924 appear in mapping pane920. The user can return to the previous designer application screen byclicking back button 930. When the user clicks one of these buttons, theuser can select the source and target using indicate on screenfunctionality similar to or the same as that of UiPath Studio™ in someembodiments. See, for example, U.S. patent application Ser. No.17/100,146. This causes a selected source screen 940 and a selectedtarget screen 950 to be displayed in mapping pane 920. It should benoted that source screen 940 and/or target screen 950 may be applicationwindows, portions of displayed applications, etc.

When the user clicks map button 932, the designer application calls oneor more AI/ML models that perform OCR and CV on source screen 940 andtarget screen 950, runs semantic AI analysis attempting to match fieldsin source screen 940 with those of target screen 950, and displaysmatches with confidence scores that meet or exceed a confidencethreshold. See FIG. 9B. A global confidence score 960 is also displayed.The mappings may be stored in an object repository in some embodimentsfor future use for the same or similar screens. See, for example, U.S.patent application Ser. No. 16/922,289.

In this example, designer application, via the AI/ML model(s), was ableto correctly match most of the fields in source screen 940 and targetscreen 950. The Currency field of source screen 940 was left blank, so amatch was not attempted for this graphical element. However, matches forthe Company field and the Invoice #field were not found, and this isindicated to the developer by displaying these elements highlighted in adifferent color and with a confidence score of 0.

The developer is able to manually match fields that were not matched bythe AI/ML model(s), and the user matching information is automaticallystored as labeled training data for retraining of the AI/ML model(s) insome embodiments. For instance, the source and target screens may besaved, along with bounding box information (e.g., coordinates) and thecoordinates and text of labels associated with the matched fields insource screen 940 and target screen 950. This is seen, for example, inFIG. 9C, where the user has indicated that the Invoice #field of sourcescreen 940 matches the Inv. Num. field of target screen 950. This causesthe confidence score for that element and the global confidence score960 to increase accordingly.

In some embodiments, the developer may be prompted or otherwisepermitted to provide synonyms for target field names. For instance, inthe example of FIG. 9C, the user is prompted to type synonyms for theInv. Num. field in synonyms text field 952 since this element was notcorrectly identified. For instance, the developer could add “InvoiceNumber”, “Billing Number” “Invoice ID”, “Billing ID”, etc. In certainembodiments the developer may be able to enter synonyms even if a givengraphical element was correctly identified. This functionality alsoallows developers to add their own terms and context. For instance, ifthe target application is a tool that the AI/ML model has not seenbefore, the developer may add terms to make the AI/ML model moreaccurate for that tool. This information may then be used to train theAI/ML model so it becomes more accurate not only for that tool, but forsimilar words and phrases found in other target screens globally.

In certain embodiments, synonyms may be proposed to the developer. Thesecan be accepted or rejected in order to make the AI/ML model moreaccurate for that context. This allows the AI/ML model to learn bothpositive and negative examples. It also allows the AI/ML model to learndifferent subsets of synonyms, or alternative sets of synonyms, that areapplicable to a given context.

Source data other than images can also be used in some embodiments. Forexample, when the developer selects map data option 914 and clicksselect source button 926 in FIG. 9D, data source options 927 appear. SeeFIG. 9E. The developer can select the desired source data format, suchas Excel®, JavaScript® Object Notation (JSON), XAML of an RPA workflow,a comma separated variable (CSV) file, etc.

Data source 970 and target image 950 are then displayed. When thedeveloper clicks map button 932, the AI/ML model(s) attempt to match thesource information to fields in target image 950. See FIG. 9F. In thiscase, source data 970 includes the same information as source screen940. In FIG. 9G, the developer makes a similar correction to that inFIG. 9C, and global confidence score 960 improves.

Relationships between labels in the source screen and target screen maybe used to determine what a given text field is meant to represent,although the text fields may be similar to or the same as one another.This may be accomplished by assigning one or more anchors to a giventext field. For instance, because the field City appears directly to theleft of it associated text field in target screen 950 and no other textfield includes this label, the designer application and/or AI/MLmodel(s) may determine that these fields are linked, and assign the Citylabel as an anchor for the target text field. If the label does notuniquely identify the text field, one or more other graphical elementsmay be assigned as anchors, and their geometric relationships may beused to uniquely identify the given target element. See, for example,U.S. Pat. No. 10,936,351 and U.S. patent application Ser. No.17/100,146.

After the source screen or source data and the target screen have beenmapped, the user can click Create button 934 to automatically generateone or more activities in the RPA workflow that implement the desiredmapping. See FIGS. 9C and 9G. This causes the RPA workflow activities tobe automatically created. In some embodiments, the RPA workflow isimmediately executed to perform the mapping task desired by the userafter creation.

To automatically create the RPA workflow, the designer application maymake use of a UI object repository. See, for example, U.S. patentapplication Publication Ser. No. 16/922,289. A UI object repository(e.g., the UiPath Object Repository™) is a collection of UI objectlibraries, which are themselves collections of UI descriptors (e.g., fora certain version of an application and one or more screens thereof).Unified target controls for similar graphical elements can be obtainedfrom the UI object repository, which instruct the RPA robot how tointeract with a given graphical element.

Such an example is shown in FIG. 10 , which illustrates an RPA designerapplication 1000 with automatically generated activities in an RPAworkflow 1010, according to an embodiment of the present invention. Thesemantic matching AI/ML model(s) have been trained to recognizeassociations between the source screen or source data and the targetscreen, per the above. In the case of the example of FIGS. 9A-G and 10,the semantic matching AI/ML model(s) are able to determine that datafrom fields in the source screen or source data should be copied intothe matching fields in the target screen. Accordingly, RPA designerapplication 1000 knows to obtain UI descriptors for the target elementsfrom the UI object repository, add activities to RPA workflow 1010 thatclick on the target screen, click on each target field, and enter thetext from the source screen or data source into the respective matchingfields in the target screen using these UI descriptors. RPA designerapplication 1000 automatically generates one or more activities in RPAworkflow 1010 that implement this functionality. In some embodiments,the developer may not be permitted to modify these activities. However,in certain embodiments, the developer may eb able to modifyconfigurations for the activities, have full permissions for editing theactivities, etc. In some embodiments, the RPA designer applicationautomatically generates an RPA robot implementing the RPA workflow andexecutes the RPA robot so information from the source screen or sourcedata is automatically copied into the target screen without furtherdirection from the developer.

Some embodiments provide a semantic copy and paste feature that allowsdevelopers without substantial programming experience to performsemantic automation. FIG. 11A illustrates a semantic copy and pasteinterface 1100, according to an embodiment of the present invention. Insome embodiments, semantic copy and paste interface 1100 is part of anRPA designer application. However, in certain embodiments, semantic copyand paste interface 1100 is part of a stand-alone application. Semanticcopy and paste interface 1100 includes an extract data button 1110, aninput data button 1120, a copy and paste button 1130, a view extracteddata button 1140, and a close button 1150. Using semantic copy and pasteinterface 1100, a user can extract data, input data, or perform copy andpaste from a source application to a target application.

Upon clicking extract data button 1110, the designer application asksthe developer to open and indicate the application that he or she wantsto extract data from via a data extraction interface 1112. See FIG. 11B.When the user clicks indicate application button 1114 of data extractioninterface 1112, indicate on screen functionality is enabled (e.g., thesame as or similar to that provided by UiPath Studio™) The user can thenselect invoice 1116 as the data source.

After indicating the application as the source (i.e., invoice 1113 inthis example), the semantic automation logic (i.e., the semanticmatching AI/ML models(s)) can predict the type of the source using aclassification algorithm, and data extraction interface 1112 displaysits prediction of the type of the source in dropdown menu 1116. See FIG.11C. The user can confirm the prediction using confirm button 1117 orselect another type from dropdown menu 1116. See FIG. 11D. A summary1118 of the extracted data is then provided in data extraction interface1112. See FIG. 11E. The user can then select back button 1119 to returnto semantic copy and paste interface 1100 to perform data input.

Upon selecting input data button 1120, the designer application asks thedeveloper to open and indicate the application that he or she wants toextract data from via a data input interface 1122. See FIG. 11F. Whenthe user clicks indicate application button 1124 of data input interface1122, indicate on screen functionality is enabled. The user can thenselect a web invoice processing page 1123 as the target application.

After indicating web invoice processing page 1123 as the target, thesemantic automation logic can predict the type of the target using theclassification algorithm, and data input interface 1122 displays itsprediction of the type of the target in dropdown menu 1126. See FIG.11G. The user can confirm the prediction using confirm button 1127 orselect another type from dropdown menu 1126. See FIG. 11H. Afterconfirmation by the user, the designer application automaticallypopulates web browser 1123 using the extracted data. See FIG. 11I.

Users can also “copy and paste” data using copy and paste button 1130.Upon selecting copy and paste button 1130, the designer application asksthe developer to open and indicate the application that he or she wantsto input data into via a copy and paste interface 1132. See FIG. 11J.When the user clicks indicate application button 1134 of copy and pasteinterface 1132, indicate on screen functionality is enabled. The usercan then select a line item entry page 1133 as the target application.

After indicating line item entry page 1133 as the target, the semanticautomation logic can predict the type of the target using theclassification algorithm, and copy and paste interface 1132 displays itsprediction of the type of the target in dropdown menu 1136. See FIG.11K. The user can confirm the prediction using confirm button 1137 orselect another type from dropdown menu 1136. See FIG. 11L. The user canalso select from a list of potential such as spreadsheet 1135. Afterconfirmation by the user, the designer application automatically copiesdata from spreadsheet 1135 into line item entry page 1133. See FIG. 11M.

In some embodiments, the designer application, via copy and pasteinterface 1132, may prompt the user before entering a given line iteminto the target application. Such an example is shown in FIG. 11N, wherethe user reviews and approves each row before it is input into line itementry page 1133. Copy and paste interface 1132 shows a preview 1138 ofthe data that will be input from the next row of spreadsheet 1135. Ifthe user clicks row confirmation button 1139, the designer applicationinputs the line shown in preview 1138 into the corresponding fields ofline item entry page 1133.

FIG. 12 is an architectural diagram illustrating an architecture 1200 ofthe AI/ML models for performing semantic AI, according to an embodimentof the present invention. A CV model 1210 performs computer visionfunctionality to identify graphical elements in a screen and an OCRmodel 1220 performs text detection and recognition for the screen(s). Inembodiments where both a source screen and a target screen are used, CVmodel 1210 and OCR model 1220 perform CV and OCR functionality on bothscreens.

CV model 1210 and OCR model 1220 then provide types, locations, sizes,text, etc. of the detected graphical elements and text in the targetscreen or both the target and source screen to a label matching model1230 that matches labels from OCR model 1220 with graphical elementsfrom CV model 1210. Matching labels and the associated graphicalelements from the screen(s) are then passed to an input data matchingmodel 1240, which matches input data from a data source or the sourcescreen with labels of graphical elements in the target screen. Thematches and the respective confidences are then provided as output frominput data matching model 1240. In some embodiments, multiple AI/MLmodels may be used for input data matching that perform matching indifferent ways (e.g., they have different neural network architectures,employ different strategies, have been trained on different trainingdata, etc.).

In some embodiments, the AI/ML model(s) may learn that fields with thesame labels may have different context. For instance, both a billinginformation and a shipping information section of a screen may have an“Address” label, but the AI/ML model may learn that the pattern of theelements near one differs from those near the other. These sections ofthe screen may then be used as anchors in a multi-anchor technique wherethe text field is the target and the “Address” label and the sectionwith the recognized pattern are the anchors. See, for example, U.S. Pat.No. 10,936,351 and U.S. patent application Ser. No. 17/100,146.

FIG. 13 is a flowchart illustrating a process 1300 for performingsemantic matching between a source screen or source data and a targetscreen using semantic AI for RPA workflows, according to an embodimentof the present invention. The process begins with receiving a selectionof a source screen or source data at 1305 and receiving a selection of atarget screen at 1310. One or more AI/ML models that have been trainedto perform semantic matching between labels in the source screen andlabels in the target screen, between data elements in the source dataand the labels in the target screen, or both, are then called at 1315.In some embodiments, the one or more AI/ML models are trained byproviding words and phrases with semantic associations between the wordsand phrases such that similar words and phrases for a given word orphrase can be identified, and providing contextual labels pertaining toa screen in which the words and phrases appear. In some embodiments, theone or more AI/ML models include a CV model, an OCR model, a labelmatching model, and an input data matching model, where the labelmatching model matches labels detected by the OCR model with fieldsdetected by the CV model and the input data model receives the matchinglabels from the label matching model and semantically matches the dataelements from the data source or data from the fields associated withthe labels from the source screen with the fields associated with thesemantically matched labels on the target screen.

Indications of graphical elements associated with semantically matchedlabels in the target screen (e.g., locations, coordinates, type, etc.)and respective confidence scores from the one or more AI/ML models arereceived at 1320. The graphical elements associated with thesemantically matched labels, individual confidence scores, and a globalconfidence score are displayed on the target screen in a matchinginterface at 1325. For instance, the target screen may be shown andmatching elements may be highlighted or otherwise made obvious to thedeveloper. In some embodiments, connections are drawing between matchingfields in the source screen or source data and the target screen. Incertain embodiments, elements in the source screen or source data forwhich no match was found are highlighted or otherwise indicated to thedeveloper.

Correction(s) to a graphical element in the target screen identified bythe one or more AI/ML models as having an associated semanticallymatching label, an indication of a new element in the target screen thatwas not semantically matched to a label in the source screen by the oneor more AI/ML models, or both, are received at 1330. Informationpertaining to the corrected and/or newly labeled graphical element(s) inthe target screen and the associated label are collected and storedeither directly (i.e., stored directly in computing system memory) orindirectly (i.e., sent to an external system for storage) at 1335. Steps1330 and 1335 are performed if such corrections are provided by thedeveloper.

One or more activities in an RPA workflow that copy data from the fieldsin the source screen having labels that the one or more AI/ML modelsidentified as semantically matching the fields in the target screen,copy the data elements from the source data into the fields of thetarget screen having labels that the one or more AI/ML models identifiedas semantically matching the data elements from the source data, orboth, are automatically generated at 1340. An RPA robot implementing theone or more generated activities in the RPA workflow is generated anddeployed at 1345.

At runtime, the deployed RPA robot accesses UI descriptors for graphicalelements it is trying to identify to perform the automation inaccordance with the RPA workflow from a UI object repository andattempts to identify graphical elements in the target screen using theseUI descriptors. If all target graphical elements can be identified at1355, the information is copied from the source screen or data source tothe target screen at 1360. However, if all graphical elements cannot befound at 1355, the RPA robot calls the AI/ML model(s) to attempt toidentify the missing graphical element(s) and updates the UI descriptorsfor these respective graphical elements at 1365. For instance, the RPArobot may use the descriptor information provided by the AI/ML model(s)to update the respective UI descriptors for the missing elements in theUI object repository so other RPA robots will not encounter the sameissue in the future. In this sense, the system is self-healing.

FIG. 14 is a flowchart illustrating a process 1400 for performingsemantic matching between a source screen or source data and a targetscreen using semantic AI for using an attended automation interface,according to an embodiment of the present invention. The process beginswith providing a semantic copy and paste interface at 1405. Data isextracted from a source application or a data source at 1410. The typeof the source is predicted using a classification algorithm at 1415. Insome embodiments, the semantic copy and paste application waits toreceive confirmation of the prediction or a change to the prediction bya user at 1420.

An indication of the target application that the user wants to extractdata into is received at 1425. The type of the target is predicted usinga classification algorithm at 1430. In some embodiments, the semanticcopy and paste application waits to receive confirmation of theprediction or a change to the prediction by a user at 1435.

In some embodiments, the user is prompted before each data entry at1440. For instance, before entering a given data item (e.g., a line ofdata, an individual graphical element, etc.), the user may see the datato be input appear in the target application. The user may then previewand approve the entry or decline. Data from the source is then enteredinto the target application at 1445

The process steps performed in FIGS. 13 and 14 may be performed by acomputer program, encoding instructions for the processor(s) to performat least part of the process(es) described in FIGS. 13 and 14 , inaccordance with embodiments of the present invention. The computerprogram may be embodied on a non-transitory computer-readable medium.The computer-readable medium may be, but is not limited to, a hard diskdrive, a flash device, RAM, a tape, and/or any other such medium orcombination of media used to store data. The computer program mayinclude encoded instructions for controlling processor(s) of a computingsystem (e.g., processor(s) 510 of computing system 500 of FIG. 5 ) toimplement all or part of the process steps described in FIGS. 13 and 14, which may also be stored on the computer-readable medium.

The computer program can be implemented in hardware, software, or ahybrid implementation. The computer program can be composed of modulesthat are in operative communication with one another, and which aredesigned to pass information or instructions to display. The computerprogram can be configured to operate on a general purpose computer, anASIC, or any other suitable device.

It will be readily understood that the components of various embodimentsof the present invention, as generally described and illustrated in thefigures herein, may be arranged and designed in a wide variety ofdifferent configurations. Thus, the detailed description of theembodiments of the present invention, as represented in the attachedfigures, is not intended to limit the scope of the invention as claimed,but is merely representative of selected embodiments of the invention.

The features, structures, or characteristics of the invention describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, reference throughout thisspecification to “certain embodiments,” “some embodiments,” or similarlanguage means that a particular feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, appearances of the phrases“in certain embodiments,” “in some embodiment,” “in other embodiments,”or similar language throughout this specification do not necessarily allrefer to the same group of embodiments and the described features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

It should be noted that reference throughout this specification tofeatures, advantages, or similar language does not imply that all of thefeatures and advantages that may be realized with the present inventionshould be or are in any single embodiment of the invention. Rather,language referring to the features and advantages is understood to meanthat a specific feature, advantage, or characteristic described inconnection with an embodiment is included in at least one embodiment ofthe present invention. Thus, discussion of the features and advantages,and similar language, throughout this specification may, but do notnecessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.In order to determine the metes and bounds of the invention, therefore,reference should be made to the appended claims.

1. A computing system, comprising: at least one processor; at least oneaccelerator; and memory storing computer program code, wherein thecomputer program code, when executed, is configured to: cause the atleast one processor to perform Monte Carlo random walk simulation, andcause the at least one accelerator to calculate a Volumetric-Ray-Casting(VRC) estimator, and the VRC estimator is configured to: extend apseudo-particle ray along a direction of an emitted particle from eachsource and collision event through all volumes that describe a problemgeometry, or until the pseudo-particle ray is no longer statisticallysignificant within a predetermined threshold, and generate a globalfluence map for neutron and photon Monte Carlo transport based on theextended pseudo-particle ray.
 2. The computing system of claim 1,wherein a plurality of pseudo-particle rays are sampled per collisionevent to provide more complete angular coverage than a singlepseudo-particle ray.
 3. The computing system of claim 2, wherein whensampling multiple rays per source or collision event, a statisticalweight of each ray is reduced by:${F\left( {i,E} \right)} = {\frac{W\left\lbrack {1 - {\exp\left( {- {\sum_{T,i}{(E)l_{i}}}} \right)}} \right\rbrack}{N{\sum_{T,i}(E)}} \times {\exp\left\lbrack {- {\int_{0}^{❘{r^{\prime} - r}❘}{\sum_{T,i}{\left( {{r + {\Omega s}},E} \right){ds}}}}} \right\rbrack}}$where F(i, E) is an expected path length, i is a tally cell, E is anenergy of the particle emitted in a direction Ω, W is a statisticalweight of the particle at collision, Σ_(T,i)(E) is a total cross-sectionof cell i at energy E, l_(i) is a ray length through cell i, r is acollision point, r′ is a point that the ray enters cell i, the integralexpression is optical thickness, and N is a number of outgoing rayssampled per collision.
 4. The computing system of claim 1, wherein thememory further comprises a buffer and the at least one processor isfurther configured to generate the pseudo-particle ray and store thegenerated ray in the buffer.
 5. The computing system of claim 4, whereinwhen the buffer is full, the at least one processor is configured tosend the buffer to the at least one accelerator.
 6. The computing systemof claim 1, wherein the calculation of the VRC estimator on the at leastone accelerator is performed concurrently with the Monte Carlo randomwalk simulation of the at least one processor.
 7. The computing systemof claim 1, wherein a number of pseudo-particle rays per source orcollision event is chosen such that calculations by the at least oneprocessor and the at least one accelerator take equal time within threepercent, or less, of a total calculation time.
 8. A computer-implementedmethod for implementation of a Volumetric-Ray-Casting (VRC) estimator byat least one accelerator, comprising: retrieving a plurality ofpseudo-particle rays from a buffer and looking up all materialcross-sections at an energy of the ray, by the at least one accelerator;performing ray casting for each ray, by the at least one accelerator;calculating, by the at least one accelerator, an expected path lengthfor each cell that is crossed and incrementing a fluence estimate ofeach cell crossed; and generating a global fluence map for neutron andphoton Monte Carlo transport based on the incremented fluence estimate,by the at least one accelerator.
 9. The computer-implemented method ofclaim 8, wherein the calculating by the VRC estimator is performedconcurrently on the at least one accelerator with random walk simulationon at least one processor.
 10. The computer-implemented method of claim8, further comprising: sampling, by the at least one accelerator, aplurality of pseudo-particle rays per collision event to provide morecomplete angular coverage than a single pseudo-particle ray.
 11. Thecomputer-implemented method of claim 8, wherein a statistical weight ofeach ray is reduced by:${F\left( {i,E} \right)} = {\frac{W\left\lbrack {1 - {\exp\left( {- {\sum_{T,i}{(E)l_{i}}}} \right)}} \right\rbrack}{N{\sum_{T,i}(E)}} \times {\exp\left\lbrack {- {\int_{0}^{❘{r^{\prime} - r}❘}{\sum_{T,i}{\left( {{r + {\Omega s}},E} \right){ds}}}}} \right\rbrack}}$where F(i, E) is an expected path length, i is a tally cell, E is anenergy of the particle emitted in a direction Ω, W is a statisticalweight of the particle at collision, Σ_(T,i)(E) is a total cross-sectionof cell i at energy E, l_(i) is a ray length through cell i, r is acollision point, r′ is a point that the ray enters cell i, the integralexpression is optical thickness, and N is a number of outgoing rayssampled per collision.
 12. The computer-implemented method of claim 8,further comprising: generating each pseudo-particle ray, by at least oneprocessor; and storing the generated ray in the buffer, by the at leastone processor.
 13. The computer-implemented method of claim 12, whereinwhen the buffer is full, the method further comprises: sending thebuffer to the at least one accelerator, by the at least one processor.14. The computer-implemented method of claim 8, wherein the calculatingby the at least one accelerator is performed concurrently with MonteCarlo random walk simulation of at least one processor.
 15. Thecomputer-implemented method of claim 14, wherein a number ofpseudo-particle rays per source or collision event is chosen such thatcalculations by the at least one processor and the at least oneaccelerator take equal time within three percent, or less, of a totalcalculation time.
 16. A computing system, comprising: at least twoprocessors; and memory storing computer program code, wherein thecomputer program code, when executed, is configured to: cause at leastone processor of the at least two processors to perform Monte Carlorandom walk simulation, and cause at least one other processor of theplurality of processors to calculate a Volumetric-Ray-Casting (VRC)estimator, and the VRC estimator is configured to: extend apseudo-particle ray along a direction of an emitted particle from eachsource and collision event through all volumes that describe a problemgeometry or until the ray is not statistically significant based on apredetermined statistical significance, and generate a global fluencemap for neutron and photon Monte Carlo transport based on the extendedpseudo-particle ray.
 17. The computing system of claim 16, wherein aplurality of pseudo-particle rays are sampled per collision event toprovide more complete angular coverage than a single pseudo-particleray.
 18. The computing system of claim 17, wherein when samplingmultiple rays per source or collision event, a statistical weight ofeach ray is reduced by:${F\left( {i,E} \right)} = {\frac{W\left\lbrack {1 - {\exp\left( {- {\sum_{T,i}{(E)l_{i}}}} \right)}} \right\rbrack}{N{\sum_{T,i}(E)}} \times {\exp\left\lbrack {- {\int_{0}^{❘{r^{\prime} - r}❘}{\sum_{T,i}{\left( {{r + {\Omega s}},E} \right){ds}}}}} \right\rbrack}}$where F(i, E) is an expected path length, i is a tally cell, E is anenergy of the particle emitted in a direction Ω, W is a statisticalweight of the particle at collision, Σ_(T,i)(E) is a total cross-sectionof cell i at energy E, l_(i) is a ray length through cell i, r is acollision point, r′ is a point that the ray enters cell i, the integralexpression is optical thickness, and N is a number of outgoing rayssampled per collision.
 19. The computing system of claim 16, wherein thememory further comprises a buffer, and the at least one processor thatperforms the Monte Carlo random walk simulation is further configuredto: generate the pseudo-particle ray and store the generated ray in thebuffer, and when the buffer is full, send the buffer to the at least oneother processor that calculates the VRC estimator.
 20. The computingsystem of claim 16, wherein a number of pseudo-particle rays per sourceor collision event is chosen such that calculations by the at least oneprocessor that performs the Monte Carlo random walk simulation and theat least one other processor that calculates the VRC estimator takeequal time within three percent, or less, of a total calculation time.